Next Article in Journal
Novel Wearable Device for Blood Leakage Detection during Hemodialysis Using an Array Sensing Patch
Previous Article in Journal
A Multidisciplinary Approach to High Throughput Nuclear Magnetic Resonance Spectroscopy
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(6), 845;

Robust Image Restoration for Motion Blur of Image Sensors

1,2,* , 1,2
Institute of Optics and Electronics, Chinese Academy of Sciences, P.O. Box 350, Shuangliu, Chengdu 610209, China
Key Laboratory of Optical Engineering, Chinese Academy of Sciences, Chengdu 610209, China
University of Chinese Academy of Sciences, 19 A Yuquan Rd, Shijingshan District, Beijing 100039, China
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 16 April 2016 / Revised: 24 May 2016 / Accepted: 31 May 2016 / Published: 9 June 2016
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [7130 KB, uploaded 9 June 2016]   |  


Blind image restoration algorithms for motion blur have been deeply researched in the past years. Although great progress has been made, blurred images containing large blur and rich, small details still cannot be restored perfectly. To deal with these problems, we present a robust image restoration algorithm for motion blur of general image sensors in this paper. Firstly, we propose a self-adaptive structure extraction method based on the total variation (TV) to separate the reliable structures from textures and small details of a blurred image which may damage the kernel estimation and interim latent image restoration. Secondly, we combine the reliable structures with priors of the blur kernel, such as sparsity and continuity, by a two-step method with which noise can be removed during iterations of the estimation to improve the precision of the estimated blur kernel. Finally, we use a MR-based Wiener filter as the non-blind deconvolution algorithm to restore the final latent image. Experimental results demonstrate that our algorithm can restore large blur images with rich, small details effectively. View Full-Text
Keywords: blind image restoration; blur kernel estimation; edge selection; image sensor signal processing; motion deblurring blind image restoration; blur kernel estimation; edge selection; image sensor signal processing; motion deblurring

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Yang, F.; Huang, Y.; Luo, Y.; Li, L.; Li, H. Robust Image Restoration for Motion Blur of Image Sensors. Sensors 2016, 16, 845.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top